Instructions to use hf-internal-testing/tiny-random-LEDForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LEDForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LEDForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-LEDForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b901ddd52e6c9dc5c52bf881e5a23059cd3f8b709c6804b2aef38d2b3fd64f6
- Size of remote file:
- 1.34 MB
- SHA256:
- 06a667f326534cfa9348768ebe5303a1108f50b316b1ce2dc17c41eb704675da
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